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pro vyhledávání: '"Ren, Jiaqian"'
Autor:
Peng, Kun, Jiang, Lei, Peng, Hao, Liu, Rui, Yu, Zhengtao, Ren, Jiaqian, Hao, Zhifeng, Yu, Philip S.
Aspect Sentiment Triplet Extraction (ASTE) is an emerging task to extract a given sentence's triplets, which consist of aspects, opinions, and sentiments. Recent studies tend to address this task with a table-filling paradigm, wherein word relations
Externí odkaz:
http://arxiv.org/abs/2312.11152
Publikováno v:
TKDE 2023
Real-world social events typically exhibit a severe class-imbalance distribution, which makes the trained detection model encounter a serious generalization challenge. Most studies solve this problem from the frequency perspective and emphasize the r
Externí odkaz:
http://arxiv.org/abs/2310.19247
Autor:
Ren, Jiaqian, Jiang, Lei, Peng, Hao, Lyu, Lingjuan, Liu, Zhiwei, Chen, Chaochao, Wu, Jia, Bai, Xu, Yu, Philip S.
Integrating multiple online social networks (OSNs) has important implications for many downstream social mining tasks, such as user preference modelling, recommendation, and link prediction. However, it is unfortunately accompanied by growing privacy
Externí odkaz:
http://arxiv.org/abs/2209.01539
State-of-the-art Graph Neural Networks (GNNs) have achieved tremendous success in social event detection tasks when restricted to a closed set of events. However, considering the large amount of data needed for training a neural network and the limit
Externí odkaz:
http://arxiv.org/abs/2208.06973
The rising popularity of online social network services has attracted lots of research on mining social media data, especially on mining social events. Social event detection, due to its wide applications, has now become a trivial task. State-of-the-
Externí odkaz:
http://arxiv.org/abs/2205.12179
Autor:
Ren, Jiaqian, Peng, Hao, Jiang, Lei, Wu, Jia, Tong, Yongxin, Wang, Lihong, Bai, Xu, Wang, Bo, Yang, Qiang
Recently published graph neural networks (GNNs) show promising performance at social event detection tasks. However, most studies are oriented toward monolingual data in languages with abundant training samples. This has left the more common multilin
Externí odkaz:
http://arxiv.org/abs/2108.03084
Akademický článek
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Autor:
Kida, Kotaro, Minamishima, Shizuka, Wang, Huifang, Ren, JiaQian, Yigitkanli, Kazim, Nozari, Ala, Mandeville, Joseph B., Liu, Philip K., Liu, Christina H., Ichinose, Fumito
Publikováno v:
In Resuscitation October 2012 83(10):1292-1297
Akademický článek
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